Energy consumption analysis method of hydrogen fuel cell vehicle based on Internet of Vehicles cloud platform

Abstract

The invention relates to a hydrogen fuel cell vehicle energy consumption analysis method based on an Internet of Vehicles cloud platform, and the method comprises the steps: U1, collecting the data information of state parameters of a vehicle, the data information of external environment parameters and the data information of driving behavior parameters in real time when the vehicle runs on a road, data information of historical operation energy consumption parameters of the vehicle is obtained; and U2, constructing a hydrogen consumption prediction model of the vehicle based on the data information of the state parameters of the vehicle, the data information of the external environment parameters and the data information of the driving behavior parameters, and inputting the data information of the historical operation energy consumption parameters of the vehicle to optimize the parameters of the model. And obtaining an optimized hydrogen consumption prediction model of the vehicle. According to the invention, the problems of untimely energy consumption data acquisition, incomplete analysis and lack of intelligent optimization in the prior art are solved, the real-time analysis and optimization of hydrogen energy consumption are realized, the hydrogen consumption is reduced, and the vehicle operation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen fuel cell vehicles, and in particular to an energy consumption analysis method for hydrogen fuel cell vehicles based on an Internet of Vehicles cloud platform. Background Art

[0002] With the promotion and application of hydrogen fuel cell vehicles (HFCV), their widespread use in commercial vehicles faces the problem of high hydrogen consumption costs. How to effectively manage hydrogen energy consumption during vehicle operation is a technical challenge that needs to be solved urgently.

[0003] The existing energy consumption management technology of hydrogen fuel cell vehicles mainly relies on the data collected by the vehicle's internal sensor system, and the analysis work is usually carried out after the vehicle is running. It lacks real-time and intelligent optimization methods and cannot be effectively adjusted in combination with changes in the external environment (such as road conditions and weather). Therefore, the energy consumption optimization effect is limited and the energy efficiency of vehicle operation cannot be fully improved. Summary of the invention

[0004] In view of the above deficiencies in the prior art, the present invention provides an energy consumption analysis method for hydrogen fuel cell vehicles based on a vehicle networking cloud platform, which not only solves the problems of untimely energy consumption data collection, incomplete analysis, and lack of intelligent optimization in the prior art, but also realizes real-time analysis and optimization of hydrogen energy consumption, reduces hydrogen consumption, and improves vehicle operating efficiency.

[0005] In order to achieve the above-mentioned purpose and other related purposes, the technical solution provided by the present invention is as follows: an energy consumption analysis method for a hydrogen fuel cell vehicle based on a vehicle networking cloud platform, the method comprising:

[0006] U1. When the vehicle is driving on the road, data information of the vehicle's state parameters, external environment parameters and driving behavior parameters are collected in real time, and data information of the vehicle's historical operating energy consumption parameters is obtained;

[0007] U2. Based on the data information of the vehicle's state parameters, the data information of the external environment parameters and the data information of the driving behavior parameters, a vehicle hydrogen consumption prediction model is constructed, and the data information of the vehicle's historical operating energy consumption parameters is input to optimize the parameters of the model to obtain an optimized vehicle hydrogen consumption prediction model;

[0008] U3. Based on the optimized vehicle hydrogen consumption prediction model, input the data information of the vehicle state parameters, the data information of the external environment parameters and the data information of the driving behavior parameters, predict the vehicle hydrogen consumption, and obtain the predicted vehicle hydrogen consumption data information;

[0009] U4. Based on the predicted hydrogen consumption data information of the vehicle, a genetic optimization algorithm fused with random forest is used to optimize the hydrogen consumption of the vehicle to obtain optimized hydrogen consumption data information of the vehicle.

[0010] Furthermore, in step U2, constructing a vehicle hydrogen consumption prediction model includes:

[0011] U211. Based on the data information of the vehicle's state parameters, the data information of the external environment parameters and the data information of the driving behavior parameters, deduplication, denoising and outlier processing are performed, and structured unified processing is performed to obtain the processed data information of the vehicle's state parameters, the data information of the external environment parameters and the data information of the driving behavior parameters;

[0012] U212. Based on the processed data information of the vehicle's state parameters, the data information of the external environment parameters and the data information of the driving behavior parameters, establish a vehicle's hydrogen consumption prediction function E h (t),

[0013] E h (t)=α·P(t)+β·F(t)+γ·W(t)+δ·L(t)+ε,

[0014] Among them, E h (t) is the consumption of hydrogen at time t, P(t) is the output power of the combustion battery at time t, F(t) is the data information of the state parameters of the vehicle after processing at time t, W(t) is the data information of the external environment parameters after processing at time t, L(t) is the data information of the driving behavior parameters after processing at time t, α, β, γ and δ are regression coefficients, and ε is the error term;

[0015] U213. Based on the hydrogen consumption prediction function E of the vehicle h (t), predict the hydrogen consumption of the vehicle and obtain the predicted hydrogen consumption data information of the vehicle.

[0016] Furthermore, the output power P(t) of the combustion battery at the time t is,

[0017]

[0018] Among them, F(t) is the data information of the processed vehicle state parameters at time t, W(t) is the data information of the processed external environment parameters at time t, and L(t) is the data information of the processed driving behavior parameters at time t.

[0019] Furthermore, the constraints of the regression coefficients α, β, γ and δ are:

[0020]

[0021] Furthermore, the error term ε is,

[0022]

[0023] Among them, P(t) is the output power of the combustion battery at time t, F(t) is the data information of the processed vehicle state parameters at time t, W(t) is the data information of the processed external environment parameters at time t, and L(t) is the data information of the processed driving behavior parameters at time t.

[0024] Furthermore, in step U2, the input of data information of historical vehicle operation energy consumption parameters to optimize the parameters of the model includes:

[0025] U221. Based on the hydrogen consumption prediction model of the vehicle, extract the model parameters to obtain the model parameter data information of the hydrogen consumption prediction model of the vehicle;

[0026] U222. Based on the model parameter data information of the hydrogen consumption prediction model of the vehicle and the data information of the historical operation energy consumption parameters of the vehicle, a model parameter optimization function Q is established.

[0027] Wherein, x is the model parameter data information of the vehicle's hydrogen consumption prediction model, y is the data information of the vehicle's historical operating energy consumption parameters, η 1 , η 2 and η 3 Target optimization factor for vehicle energy loss;

[0028] U223. Based on the model parameter optimization function Q, the parameters of the model are optimized to obtain an optimized vehicle hydrogen consumption prediction model.

[0029] Furthermore, the data information of the vehicle's state parameters include parameter data information of vehicle speed, parameter data information of hydrogen consumption, and parameter data information of fuel cell temperature; the data information of the external environment parameters include parameter data information of vehicle speed, parameter data information of hydrogen consumption, and parameter data information of fuel cell temperature; and the data information of the driving behavior parameters include parameter data information of acceleration, parameter data information of the number of brakes, and parameter data information of turning radius.

[0030] Furthermore, in step U4, the optimization of the hydrogen consumption of the vehicle using the genetic optimization algorithm integrated with random forest includes:

[0031] U41. Based on the predicted hydrogen consumption data information of the vehicle, a decision tree function R of the hydrogen consumption of the vehicle is constructed.

[0032]

[0033] Among them, z is the predicted vehicle hydrogen consumption data information, ρ 1 , 2 and ρ 3 is the Gini index factor of the vehicle's hydrogen consumption, and feature extraction is performed on the vehicle's hydrogen consumption data to obtain feature data information of the vehicle's hydrogen consumption;

[0034] U42. Based on the characteristic data information of the hydrogen consumption of the vehicle, the parameters of the genetic population are initialized, the maximum number of iterations is determined, and the data information of the initialized genetic population is obtained;

[0035] U43. Based on the data information of the initialized genetic population, establish the fitness function G of the population individuals,

[0036]

[0037] Among them, r is the data information of the initialized genetic population, σ 1 , σ 2 and σ 3 It is the fitness determining factor of the genetic population individuals, and the fitness values ​​of the population individuals are calculated to obtain the fitness value data information of the population individuals;

[0038] U44. Based on the fitness value data information of the population individuals, establish the target optimization function H,

[0039]

[0040] Among them, g is the fitness value data information of the population individual, ω 1 ,ω 2 and ω 3 In order to optimize the factors, the hydrogen consumption of the vehicle is optimized to obtain the hydrogen consumption data information of the optimized vehicle.

[0041] Furthermore, the optimization factor ω 1 ,ω 2 and ω 3 The constraint function f is,

[0042]

[0043] Among them, the value range of the constraint function f is (3,4).

[0044] Furthermore, the fitness determining factor σ of the individual in the genetic population is 1 , σ 2 and σ 3 for,

[0045]

[0046] Among them, r is the data information of the initialized genetic population.

[0047] The present invention has the following positive effects:

[0048] 1. The present invention constructs a vehicle's hydrogen consumption prediction model, and inputs data information of the vehicle's historical operating energy consumption parameters to optimize the model's parameters, thereby obtaining an optimized vehicle's hydrogen consumption prediction model. This model not only collects and processes multi-source data in real time, but also provides a more comprehensive analysis, thereby solving the problems of untimely energy consumption data collection, incomplete analysis, and lack of intelligent optimization in the prior art, and automatically adjusts the vehicle's energy management system to achieve optimal optimization of energy consumption.

[0049] 2. The present invention optimizes the hydrogen consumption of the vehicle by adopting a genetic optimization algorithm integrated with random forest to obtain optimized hydrogen consumption data information of the vehicle, which not only realizes real-time analysis and optimization of hydrogen energy consumption, reduces hydrogen consumption, and improves vehicle operation efficiency, but also generates dynamic adjustment strategies according to the real-time data of the vehicle to further reduce the consumption of hydrogen energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0051] Figure 2 A schematic diagram of a process for constructing a hydrogen consumption prediction model for a vehicle according to the present invention;

[0052] Figure 3 A schematic diagram of a process for optimizing parameters of a model according to the present invention;

[0053] Figure 4 It is a schematic diagram of the flow of the genetic optimization algorithm fused with random forest of the present invention. DETAILED DESCRIPTION

[0054] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0055] Example 1: Figure 1 As shown, a method for analyzing energy consumption of a hydrogen fuel cell vehicle based on a vehicle networking cloud platform comprises:

[0056] U1. When the vehicle is driving on the road, data information of the vehicle's state parameters, external environment parameters and driving behavior parameters are collected in real time, and data information of the vehicle's historical operating energy consumption parameters is obtained;

[0057] U2. Based on the data information of the vehicle's state parameters, the data information of the external environment parameters and the data information of the driving behavior parameters, a vehicle hydrogen consumption prediction model is constructed, and the data information of the vehicle's historical operating energy consumption parameters is input to optimize the parameters of the model to obtain an optimized vehicle hydrogen consumption prediction model;

[0058] U3. Based on the optimized vehicle hydrogen consumption prediction model, input the data information of the vehicle state parameters, the data information of the external environment parameters and the data information of the driving behavior parameters, predict the vehicle hydrogen consumption, and obtain the predicted vehicle hydrogen consumption data information;

[0059] U4. Based on the predicted hydrogen consumption data information of the vehicle, a genetic optimization algorithm fused with random forest is used to optimize the hydrogen consumption of the vehicle to obtain optimized hydrogen consumption data information of the vehicle.

[0060] In this embodiment, if Figure 2 As shown, in step U2, constructing a vehicle's hydrogen consumption prediction model includes:

[0061] U211. Based on the data information of the vehicle's state parameters, the data information of the external environment parameters and the data information of the driving behavior parameters, deduplication, denoising and outlier processing are performed, and structured unified processing is performed to obtain the processed data information of the vehicle's state parameters, the data information of the external environment parameters and the data information of the driving behavior parameters;

[0062] U212. Based on the processed data information of the vehicle's state parameters, the data information of the external environment parameters and the data information of the driving behavior parameters, establish a vehicle's hydrogen consumption prediction function E h (t),

[0063] E h (t)=α·P(t)+β·F(t)+γ·W(t)+δ·L(t)+ε,

[0064] Among them, E h(t) is the consumption of hydrogen at time t, P(t) is the output power of the combustion battery at time t, F(t) is the data information of the state parameters of the vehicle after processing at time t, W(t) is the data information of the external environment parameters after processing at time t, L(t) is the data information of the driving behavior parameters after processing at time t, α, β, γ and δ are regression coefficients, and ε is the error term;

[0065] U213. Based on the hydrogen consumption prediction function E of the vehicle h (t), predict the hydrogen consumption of the vehicle and obtain the predicted hydrogen consumption data information of the vehicle.

[0066] In this embodiment, the output power P(t) of the combustion battery at time t is,

[0067]

[0068] Among them, F(t) is the data information of the processed vehicle state parameters at time t, W(t) is the data information of the processed external environment parameters at time t, and L(t) is the data information of the processed driving behavior parameters at time t.

[0069] In this embodiment, the constraints of the regression coefficients α, β, γ and δ are:

[0070]

[0071] In this embodiment, the error term ε is,

[0072]

[0073] Among them, P(t) is the output power of the combustion battery at time t, F(t) is the data information of the processed vehicle state parameters at time t, W(t) is the data information of the processed external environment parameters at time t, and L(t) is the data information of the processed driving behavior parameters at time t.

[0074] In this embodiment, if Figure 3 As shown, in step U2, the input of data information of historical running energy consumption parameters of the vehicle to optimize the parameters of the model includes:

[0075] U221. Based on the hydrogen consumption prediction model of the vehicle, extract the model parameters to obtain the model parameter data information of the hydrogen consumption prediction model of the vehicle;

[0076] U222. Based on the model parameter data information of the hydrogen consumption prediction model of the vehicle and the data information of the historical operation energy consumption parameters of the vehicle, a model parameter optimization function Q is established.

[0077] Wherein, x is the model parameter data information of the vehicle's hydrogen consumption prediction model, y is the data information of the vehicle's historical operating energy consumption parameters, η 1 , η 2 and η 3 Target optimization factor for vehicle energy loss;

[0078] U223. Based on the model parameter optimization function Q, the parameters of the model are optimized to obtain an optimized vehicle hydrogen consumption prediction model.

[0079] In this embodiment, the data information of the vehicle's state parameters include parameter data information of vehicle speed, parameter data information of hydrogen consumption, and parameter data information of fuel cell temperature; the data information of the external environment parameters include parameter data information of vehicle speed, parameter data information of hydrogen consumption, and parameter data information of fuel cell temperature; the data information of the driving behavior parameters include parameter data information of acceleration, parameter data information of the number of brakes, and parameter data information of the turning radius.

[0080] Example 2: Based on the energy consumption analysis method of a hydrogen fuel cell vehicle based on the Internet of Vehicles cloud platform in Example 1, the present invention is further illustrated and described below.

[0081] like Figure 1 As shown, a method for analyzing energy consumption of a hydrogen fuel cell vehicle based on a vehicle networking cloud platform comprises:

[0082] U1. When the vehicle is driving on the road, data information of the vehicle's state parameters, external environment parameters and driving behavior parameters are collected in real time, and data information of the vehicle's historical operating energy consumption parameters is obtained;

[0083] U2. Based on the data information of the vehicle's state parameters, the data information of the external environment parameters and the data information of the driving behavior parameters, a vehicle hydrogen consumption prediction model is constructed, and the data information of the vehicle's historical operating energy consumption parameters is input to optimize the parameters of the model to obtain an optimized vehicle hydrogen consumption prediction model;

[0084] U3. Based on the optimized vehicle hydrogen consumption prediction model, input the data information of the vehicle state parameters, the data information of the external environment parameters and the data information of the driving behavior parameters, predict the vehicle hydrogen consumption, and obtain the predicted vehicle hydrogen consumption data information;

[0085] U4. Based on the predicted hydrogen consumption data information of the vehicle, a genetic optimization algorithm fused with random forest is used to optimize the hydrogen consumption of the vehicle to obtain optimized hydrogen consumption data information of the vehicle.

[0086] In this embodiment, if Figure 4 As shown, in step U4, the optimization of the hydrogen consumption of the vehicle by using the genetic optimization algorithm fused with random forest includes:

[0087] U41. Based on the predicted hydrogen consumption data information of the vehicle, a decision tree function R of the hydrogen consumption of the vehicle is constructed.

[0088]

[0089] Among them, z is the predicted vehicle hydrogen consumption data information, ρ 1 , 2 and ρ 3 is the Gini index factor of the vehicle's hydrogen consumption, and feature extraction is performed on the vehicle's hydrogen consumption data to obtain feature data information of the vehicle's hydrogen consumption;

[0090] U42. Based on the characteristic data information of the hydrogen consumption of the vehicle, the parameters of the genetic population are initialized, the maximum number of iterations is determined, and the data information of the initialized genetic population is obtained;

[0091] U43. Based on the data information of the initialized genetic population, establish the fitness function G of the population individuals,

[0092]

[0093] Among them, r is the data information of the initialized genetic population, σ 1 , σ 2 and σ 3 It is the fitness determining factor of the genetic population individuals, and the fitness values ​​of the population individuals are calculated to obtain the fitness value data information of the population individuals;

[0094] U44. Based on the fitness value data information of the population individuals, establish the target optimization function H,

[0095]

[0096] Among them, g is the fitness value data information of the population individual, ω 1 ,ω 2 and ω 3 In order to optimize the factors, the hydrogen consumption of the vehicle is optimized to obtain the hydrogen consumption data information of the optimized vehicle.

[0097] In this embodiment, the optimization factor ω 1 ,ω 2 and ω 3 The constraint function f is,

[0098]

[0099] Among them, the value range of the constraint function f is (3,4).

[0100] In this embodiment, the fitness determining factor σ of the genetic population individuals 1 , σ 2 and σ 3 for,

[0101]

[0102] Among them, r is the data information of the initialized genetic population.

[0103] In this embodiment, the present invention provides an energy consumption analysis system for hydrogen fuel cell vehicles based on a vehicle networking cloud platform, including a computer device that is programmed or configured to execute any step of the energy consumption analysis method for hydrogen fuel cell vehicles based on a vehicle networking cloud platform.

[0104] In this embodiment, the present invention provides a computer-readable storage medium, which stores a computer program programmed or configured to execute any one of the energy consumption analysis methods for hydrogen fuel cell vehicles based on the Internet of Vehicles cloud platform.

[0105] Any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM).

[0106] In summary, the present invention not only solves the problems of untimely energy consumption data collection, incomplete analysis, and lack of intelligent optimization in the prior art, but also realizes real-time analysis and optimization of hydrogen energy consumption, reduces hydrogen consumption, and improves vehicle operation efficiency.

[0107] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for analyzing energy consumption of hydrogen fuel cell vehicles based on a vehicle networking cloud platform, characterized in that: The method comprises: U1. When the vehicle is driving on the road, data information of the vehicle's state parameters, external environment parameters and driving behavior parameters are collected in real time, and data information of the vehicle's historical operating energy consumption parameters is obtained; U2. Based on the data information of the vehicle's state parameters, the data information of the external environment parameters and the data information of the driving behavior parameters, a vehicle hydrogen consumption prediction model is constructed, and the data information of the historical operating energy consumption parameters of the vehicle is input to optimize the parameters of the model to obtain an optimized vehicle hydrogen consumption prediction model; U3. Based on the optimized vehicle hydrogen consumption prediction model, input the data information of the vehicle state parameters, the data information of the external environment parameters and the data information of the driving behavior parameters, predict the vehicle hydrogen consumption, and obtain the predicted vehicle hydrogen consumption data information; U4. Based on the predicted hydrogen consumption data information of the vehicle, a genetic optimization algorithm fused with random forest is used to optimize the hydrogen consumption of the vehicle to obtain optimized hydrogen consumption data information of the vehicle.

2. The energy consumption analysis method of a hydrogen fuel cell vehicle based on a vehicle networking cloud platform according to claim 1 is characterized in that: In step U2, the construction of the vehicle's hydrogen consumption prediction model includes: U211. Based on the data information of the vehicle's state parameters, the data information of the external environment parameters and the data information of the driving behavior parameters, deduplication, denoising and outlier processing are performed, and structured unified processing is performed to obtain the processed data information of the vehicle's state parameters, the data information of the external environment parameters and the data information of the driving behavior parameters; U212. Based on the processed data information of the vehicle's state parameters, the data information of the external environment parameters and the data information of the driving behavior parameters, establish a vehicle's hydrogen consumption prediction function E h (t), E h (t)=α·P(t)+β·F(t)+γ·W(t)+δ·L(t)+ε, Among them, E h (t) is the consumption of hydrogen at time t, P(t) is the output power of the combustion battery at time t, F(t) is the data information of the state parameters of the vehicle after processing at time t, W(t) is the data information of the external environment parameters after processing at time t, L(t) is the data information of the driving behavior parameters after processing at time t, α, β, γ and δ are regression coefficients, and ε is the error term; U213. Based on the hydrogen consumption prediction function E of the vehicle h (t), predict the hydrogen consumption of the vehicle and obtain the predicted hydrogen consumption data information of the vehicle.

3. The energy consumption analysis method of a hydrogen fuel cell vehicle based on the Internet of Vehicles cloud platform according to claim 2 is characterized in that: The output power P(t) of the combustion battery at the time t is, Among them, F(t) is the data information of the processed vehicle state parameters at time t, W(t) is the data information of the processed external environment parameters at time t, and L(t) is the data information of the processed driving behavior parameters at time t.

4. The energy consumption analysis method of a hydrogen fuel cell vehicle based on a vehicle networking cloud platform according to claim 2 is characterized in that: The constraints of the regression coefficients α, β, γ and δ are:

5. The energy consumption analysis method of a hydrogen fuel cell vehicle based on the Internet of Vehicles cloud platform according to claim 2 is characterized in that: The error term ε is, Among them, P(t) is the output power of the combustion battery at time t, F(t) is the data information of the processed vehicle state parameters at time t, W(t) is the data information of the processed external environment parameters at time t, and L(t) is the data information of the processed driving behavior parameters at time t.

6. The energy consumption analysis method of hydrogen fuel cell vehicles based on the Internet of Vehicles cloud platform according to claim 1 is characterized in that: In step U2, the input of data information of historical vehicle operation energy consumption parameters to optimize the parameters of the model includes: U221. Based on the hydrogen consumption prediction model of the vehicle, extract the model parameters to obtain the model parameter data information of the hydrogen consumption prediction model of the vehicle; U222. Based on the model parameter data information of the hydrogen consumption prediction model of the vehicle and the data information of the historical operation energy consumption parameters of the vehicle, a model parameter optimization function Q is established. Among them, x is the model parameter data information of the vehicle's hydrogen consumption prediction model, y is the data information of the vehicle's historical operating energy consumption parameters, η1, η2 and η3 are the target optimization factors of the vehicle's energy consumption loss; U223. Based on the model parameter optimization function Q, the parameters of the model are optimized to obtain an optimized vehicle hydrogen consumption prediction model.

7. The energy consumption analysis method of a hydrogen fuel cell vehicle based on a vehicle networking cloud platform according to claim 1 is characterized in that: The data information of the vehicle's state parameters includes parameter data information of vehicle speed, parameter data information of hydrogen consumption and parameter data information of fuel cell temperature; the data information of the external environment parameters includes parameter data information of vehicle speed, parameter data information of hydrogen consumption and parameter data information of fuel cell temperature; the data information of the driving behavior parameters includes parameter data information of acceleration, parameter data information of the number of brakes and parameter data information of turning radius.

8. The energy consumption analysis method of hydrogen fuel cell vehicles based on the Internet of Vehicles cloud platform according to claim 1 is characterized in that: In step U4, the optimization of the hydrogen consumption of the vehicle using the genetic optimization algorithm integrated with random forest includes: U41. Based on the predicted hydrogen consumption data information of the vehicle, a decision tree function R of the hydrogen consumption of the vehicle is constructed. Among them, z is the predicted hydrogen consumption data information of the vehicle, ρ1, ρ2 and ρ3 are the Gini index factors of the hydrogen consumption of the vehicle, and feature extraction is performed on the hydrogen consumption data of the vehicle to obtain feature data information of the hydrogen consumption of the vehicle; U42. Based on the characteristic data information of the hydrogen consumption of the vehicle, the parameters of the genetic population are initialized, the maximum number of iterations is determined, and the data information of the initialized genetic population is obtained; U43. Based on the data information of the initialized genetic population, establish the fitness function G of the population individuals, Among them, r is the data information of the initialized genetic population, σ1, σ2 and σ3 are the fitness determining factors of the genetic population individuals, and the fitness values ​​of the population individuals are calculated to obtain the fitness value data information of the population individuals; U44. Based on the fitness value data information of the population individuals, establish the target optimization function H, Among them, g is the fitness value data information of the population individual, ω1, ω2 and ω3 are optimization factors, and the hydrogen consumption of the vehicle is optimized to obtain the optimized hydrogen consumption data information of the vehicle.

9. The energy consumption analysis method of a hydrogen fuel cell vehicle based on the Internet of Vehicles cloud platform according to claim 8 is characterized in that: The constraint function f of the optimization factors ω1, ω2 and ω3 is, Among them, the value range of the constraint function f is (3,4).

10. The energy consumption analysis method of a hydrogen fuel cell vehicle based on a vehicle networking cloud platform according to claim 8, characterized in that: The fitness determining factors σ1, σ2 and σ3 of the individuals in the genetic population are: Among them, r is the data information of the initialized genetic population.

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